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WO2018144571A2 - Diagnostic pour distinguer des infections bactériennes - Google Patents

Diagnostic pour distinguer des infections bactériennes Download PDF

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Publication number
WO2018144571A2
WO2018144571A2 PCT/US2018/016185 US2018016185W WO2018144571A2 WO 2018144571 A2 WO2018144571 A2 WO 2018144571A2 US 2018016185 W US2018016185 W US 2018016185W WO 2018144571 A2 WO2018144571 A2 WO 2018144571A2
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WO
WIPO (PCT)
Prior art keywords
peptides
bacterial
infection
response
seq
Prior art date
Application number
PCT/US2018/016185
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English (en)
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WO2018144571A3 (fr
Inventor
Stephen Johnston
Lu Wang
Original Assignee
Arizona Board Of Regents On Behalf Of Arizona State University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Arizona Board Of Regents On Behalf Of Arizona State University filed Critical Arizona Board Of Regents On Behalf Of Arizona State University
Priority to US16/480,887 priority Critical patent/US10712342B2/en
Publication of WO2018144571A2 publication Critical patent/WO2018144571A2/fr
Publication of WO2018144571A3 publication Critical patent/WO2018144571A3/fr
Priority to US16/896,117 priority patent/US11360086B2/en
Priority to US17/806,242 priority patent/US20220341930A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56911—Bacteria
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56983—Viruses
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2469/00—Immunoassays for the detection of microorganisms
    • G01N2469/20—Detection of antibodies in sample from host which are directed against antigens from microorganisms

Definitions

  • Antibiotic resistance is a global problem mainly due to the overuse of antibiotics in clinical settings. Overuse is mainly caused by the lack of accurate diagnosis that can distinguish bacterial infections from other types of infections. This is especially true for respiratory tract infections and pediatric sepsis. More accurate diagnosis at the time of an initial clinical visit that can distinguish bacterial from other infections would greatly curb the antibiotic overuse problem.
  • the array comprises two peptides, which first peptide comprises SEQ ID NO. l and said second peptide comprises SEQ ID NO.2. Further, the first peptide comprises a motif able to be bind to a plurality of bacterial specific antibodies, wherein the motif comprises SEQ ID NO.3 and the second peptide comprises a first motif and a second motif able to be bind to a plurality of bacterial specific antibodies, wherein the first motif comprises SEQ ID NO.4 and the second motif comprises SEQ ID NO.5.
  • a method to distinguish a bacterial infection from a viral infection contains the steps of contacting an antibody-containing sample with an array of immobilized peptides, wherein said peptides are selected from a group consisting of one or more peptides that bind to antibodies produced in response to a bacterial infection and one or more peptides that bind to antibodies produced in response to a viral infection; and detecting binding of an antibody from said sample with a peptide from said group.
  • FIG. 1 Hierarchical clustering for the correlation of the whole immunosignature by type of infection shows potential classification of bacterial versus viral infection. Correlation is calculated for each pair-wise sample comparison, then the samples that belong to the same class are averaged to a single correlation value. The clustering table shows most viruses can be distinguished from the bacteria, with the exception of flu.
  • Figures 2a-2d Performance of distinguishing bacterial versus viral infection, (a). PCA analysis on the selected peptides shows one factor is responsible for most variability, test set samples are highlighted in the right figure, (b). Clustering of the selected peptides shows most peptides are bacteria specific peptides, (c) Performance of the classification algorithms, (d) Two selected peptides can achieve similar performance of classification.
  • Figures 3a-3c Performance of distinguishing bacterial versus viral and other types of infection, (a). PCA analysis on the selected peptides shows one factor is responsible for most variability, test set samples are highlighted in the right figure, (b). Clustering of the selected peptides shows most peptides are bacteria specific peptides, (c) Performance of the classification algorithms.
  • Figure 4 Hierarchical clustering for the correlation of the whole immunosignature by type of infection including all classes. Non-infected class is more similar to bacterial infection, while the non-bacterial and non-viral infections are spread out in groups.
  • Figure 5 Scatterplot of the 2 selected peptides. Color is true class. All samples are included in this figure. Both peptides are bacteria specific peptides.
  • Figure 6 Hierarchical clustering for the correlation of the whole immunosignature of each sample within bacterial and viral infections. More virus samples are misclassified as bacteria and mostly are influenza samples. Specificity for virus is nearly 100%.
  • Figure 7 Hierarchical clustering for the correlation of the whole immunosignature of each sample within bacterial and viral infections. More virus samples are misclassified as bacteria and mostly are influenza samples. Specificity for virus is nearly 100%.
  • Figure 8 Probability graph for being virus using Neural Network method in bacteria vs viral infection experiment. Color is true label. All samples are included in this figure. Graph shows good separation between the two groups.
  • Immunosignature is a peptide microarray that derives peptide sequences from random space rather than biological sequence space.
  • the analysis of semi-random sequences allows for a mostly unbiased search for antibodies that may display a common binding motif.
  • the applicant would not focus on sequences for any given pathogen, which allows exploring more broadly for antibodies that may fall into a pattern that overlaps bacteria and virus.
  • the immunosignatures of 40 different types of pathogens were examined. Each type of infection has a signature that distinguishes it from people without the infection and from other types of infection. Immunosignature by measuring the antibody response against pathogens, can distinguish bacterial from viral infections. Further, the applicant was able to identify 2 peptides that can distinguish the two classes, which would yield a biomarker with more clinical utility. Further, immunosignature can distinguish bacterial from generally a non-bacterial infection, which also has clinical relevance, since there are always non-bacterial and non-viral infections present in clinical settings.
  • Immunosignatures are patterns of antibody binding on 125,000 peptide feature chips. The peptides are chosen from random peptide sequence space to maximize chemical diversity for discriminating antibody binding. Immunosignatures have been demonstrated to readily distinguish different types of infections and chronic diseases.
  • a training set of x samples of bacteria, viral and non-infected were used to establish the signature. The training set was validated on an independent set of samples and then tested on another completely independent set. A set of 1000 peptides were identified that could make the distinction with X specificity and Y sensitivity in the test set. Most of the misclassifications were influenza samples called as bacterial infections.
  • sample includes any biological specimen obtained from an individual. Suitable samples for use in the present invention include, without limitation, whole blood, plasma, serum, saliva, urine, stool (i.e., feces), tears, and any other bodily fluid, or a tissue sample (i.e., biopsy) such as a small intestine or colon sample, and cellular extracts thereof (e.g., red blood cellular extract).
  • tissue sample i.e., biopsy
  • the sample is a blood, plasma, or serum sample.
  • sample is a serum sample. They represent a wide range of bacterial and virus species. There were between 9-22 sera samples from each type of pathogen.
  • Table 1 Sample information used in this application. 12 classes of infections are included in addition to a group of non-infected individuals coded as normal. Class of type of sample Count per
  • FIG. 1 demonstrates the initial unsupervised division showing that influenza virus is the sole misclassified group, classified with bacteria.
  • FIG.6 A further breakdown per samples is shown in FIG.6.
  • Hierarchical clustering using the correlations for every sample (no sample is averaged) is shown in FIG.6.
  • the specificity for viral infections is close to 100%, with some viruses being classified as bacteria, mostly influenza. This result is consistent with the class level clustering result.
  • the diagnostic peptides are chosen against a background of non-diagnostic peptides on the arrays.
  • the diagnostic peptides can be arrayed with a set of 100-10,000 random peptides. These could be mixed or individual and spotted separately or as a mixture.
  • PCA Principle Component Analysis
  • test set samples are also highlighted in the clustering heatmap to show their clustering group location compared with the training and validation set. No obvious overfitting is noted as test set samples are generally clustered in the right class.
  • Machine learning classifiers like Random Forest and Neural Networks are used to build the model of classification between the two groups.
  • a model is trained using training data and a validation set is used to fine-tune the model and gain an initial performance evaluation to limit overfitting.
  • training group only usually results in overfitting because the classifier might adjust to the random variations in the training group to gain best fit scores.
  • Validation set only also pose the same issue because the model is generated with information from the validation dataset. In microarray studies, there are inevitably more variables than observations, overfitting becomes more pronounced. Independent datasets are needed to test the performance of the classifier, the test set data are never used in feature selection to model generating and is only used for the final evaluation of the model.
  • Random Forest and Neural Networks both have minimal misclassification rate on both training and validation.
  • the final performance on the test set is also similar for both classifiers.
  • Random Forest tends to exhibit less sensitivity to the bacterial infections (sensitivity at 0.58) but is extremely specific (0.95). This is a bias toward true negatives as the cost of lower true positives.
  • Neural Network models yielded more balance for TP and FP between the two groups, with sensitivity and specificity at 0.83 and 0.84 respectively (FIG. 8). Both models yield misclassification rates of less than 20%. Since it may be that up to 60% of human infections are from virus, if doctors can distinguish viral infections from bacterial infections, the use of antibiotics could be reduced by over 50%.
  • Stepwise regression is utilized to find the optimal, non-redundant peptides that can be used to fit the model.
  • Each peptide has to meet a p-value cutoff of 0.1 to enter the model and will exit the model upon the exceeding the existing cutoff p-value of 0.1.
  • Regression is started assuming all peptides are out of the model. The whole process is iterated until the model is stabilized, meaning, i.e., no peptides leave or enter the model. Then the model is fine-tuned to maximize RSquare for the Validation set (FIG. 2d).
  • the final model only includes two peptides, GALSRSFANVSFPGVAG (SEQ ID. No.
  • Table 2 Performance of bacterial vs non-bacterial infection classification using 5 selected peptides. Peptides are selected from stepwise regression using mixed p-value model at cutoff of 0.1. Logistic fit is then performed using the selected peptides. Test set performance is much lower compared with the complete model using all selected peptides from T-Test. logistic Fit Training Validation Test
  • Example 2 Epitopes of bacteria were identified via blast search of the two peptides followed by ungapped motif mapping
  • peptide 2 SEQ ID No. 2. It is interesting to note that for peptide 1 (SEQ ID No. 1), only 6 amino acids seem to be the target of bacterial specific antibodies. As for peptide 2 (SEQ ID No. 2), the full length of the peptide could be the target of bacterial specific antibodies.
  • Each epitope is matched with at least 20 sequences from the bacterial proteome, so the epitopes are broadly represented in the bacterial world.
  • Peptide 1 (SEQ ID No. 1) has 1 epitope (SEQ ID No. 3) with length of 6 amino acid (a.a). While peptide 2 (SEQ ID No. 2) has 2 matched epitopes (SEQ ID No. 4 and SEQ ID No. 5) with length of 8 a.a. and 6 a.a. correspondingly. Matched part is highlighted with color in peptides. This implies only part of peptide 1 is identified by bacterial specific antibody while the whole sequence of peptide 2 is the target for bacterial antibodies.
  • Example 3 Broad bacterial versus non-bacterial infection classifier shows robust distinction and better performance
  • the better performing Neural Network model is at 0.83 sensitivity and 0.94 specificity for bacteria with a Generalized RSquare of 0.73, all of which is a significant increase compared to the original bacterial vs viral model.
  • This improvement can be the result of more samples being used for model construction, or it can be by including more types of infection as the non-bacteria comparison, which can lead to a more robust bacterial specific signature.
  • Immunosignature a microarray-based serological test that uses semi-random peptides to splay out the antibody repertoire from infected individuals, is used to distinguish viral infections from bacterial infections.
  • Immunosignatures can detect peptides that generally separate bacterial infections from viral infections.
  • Machine learning models were used to identify the predictive performance of a given set of peptides across 169 patients, of which 105 patients have bacterial infections and 64 viral infections.
  • Correlation of the infections are used to first test the possibility of distinction at the antibody system level.
  • the logic behind using correlation of infections is that the immune system might systematically see the difference between bacterial and viral infection by activating different pathways. Immunosignature platforms measure antibody repertoire in the blood. If you use all the data from the platform, then you are measuring the immune system. Correlation of the immune system can then be tested by calculating the correlation of the immunosignature for different pathogens. The result from the correlation offer insights into understanding both diagnosis and how the immune system works. It seems the immune system is able to distinguish most bacterial and viral infections and mount totally different immune response, since only one infection is misclassified. This confirms the notion that our immune system probably knows the source of the infection and respond accordingly.
  • the immune system does not know the source of infection but because all infections within the same class are so similar, the immune system always produces similar antibodies against various bacterial infections. The same might be the case for viral infection.
  • most of the signatures that can distinguish bacterial and viral infection are bacterial specific signatures, implying the immune system is producing various antibodies against bacterial infection in ways analogous to broad-spectrum antibiotics.
  • influenza virus is misclassified into bacteria is interesting because it suggests somehow influenza virus successfully tricked the immune system into thinking it is bacteria such that antibodies against bacteria are produced, the result of which will be ineffective. This is consistent with the fact that viruses are highly contagious worldwide, implying the immune system cannot quickly mount an effective immune response because influenza virus is regarded as "bacteria.” This misclassification by the immune system might also explain why there are already pre-existing neutralizing antibodies within the immune system, but they were not usually elucidated during flu infection.
  • the two peptides were further examined by identifying matched sequences from bacteria proteomes and then identifying consensus motifs with the matched sequences. These consensus motifs could be the binding target within the two peptides on the immunosignature. Only 6 a.a. of consensus motif in one of the peptides is identified and the full length of the other peptide is matched by bacterial antibodies. This indicates there could be redundancy in these two peptides.
  • Serum samples were collected at various source described in detail below and received at Arizona State University (ASU) under Institutional Review Board Protocol # 0912004625, "Profiling Serum for Unique Antibody Signatures". All samples have informed consent and were anonymized. Every disease sample was tested positive for the specified disease before rendering to ASU. Bordetella pertussis samples were provided by Seracare Life Sciences (Seracare). Tuberculosis from University of Texas at El Paso (UTEP). Malaria from Seracare. HIV from Creative Testing Solutions (CTS). Flu from BioreclamationlVT. Dengue from UTEP. WNV from CTS. VF from Sonora Lab. Chagas from CTS. Lyme from Seracare. Hepatitis B from CTS, Syphilis from Seracare.
  • Serum samples were diluted 1 : 1500 into the sample buffer (3% BSA in lx PBST) before incubated on immunosignature microarrays at a final volume of 150ul for lh at 37 °C with rotating. Primary antibodies from the serum were then washed with lx PBST for 3 times and rinsed with ddH 2 0 for 3 times. 4nM Secondary anti-human IgG antibodies with Alexa-Fluor 555 conjugation from Life Technologies are added in secondary incubation buffer (0.75% Casein in lx PBST with 0.05% Tween20) to detect primary antibody binding. Secondary antibodies were incubated on the array for lh at 37 °C before washed off with blocking buffer. Slides were then washed with lx PBST and ddH 2 0 before drying. Images were obtained from scanning arrays at 555nm using Innoscan 910 scanner. Signal intensity for features were extracted using GenePix Pro 6.0.
  • Analysis is performed using scripts written in R or the JMP software (SAS Institute Inc.). Raw intensity reads for all samples are normalized to the median per sample. Quality Control (QC) for the samples is performed by checking each sample' s average correlation against all other samples. Samples with correlation ⁇ 0.2 are deleted. 226 samples are run on immunosignature and 212 samples passed QC and were analyzed.
  • QC Quality Control
  • Feature selection is done by using samples in the training and validation set. Two-tail Student' s T-Test is performed for each peptides by comparing bacterial infection samples versus viral infection samples (non-bacterial infection samples). Cutoff is controlled at allowing 1 false positive for all test, which is 1/124,000 or 1000 peptides, whichever is smaller.
  • PCA is performed using selected peptides with all samples, with the test set samples highlighted in right PCA plot.
  • Hierarchical clustering is performed using the selected peptides with all samples. Ward method is used in calculating the distance between the samples. The same method is used in calculating distance for the features in two-way clustering.
  • Random Forest is carried out with maximum 100 trees in the forest. Minimum split per tree is set at 10 and maximum at 2000. Early stopping rule is applied on validation set. And performance of the classifier is evaluated and output as confusion matrix for the training, validation and test set. Neural Network is built with one hidden layer and 3 nodes, with TanH as the activation function.
  • Stepwise regression for reducing number of features is used with stopping rule of p-value cutoff at 0.1 for both entering and leaving the model. The model starts empty with no feature. Features become included in the model if below cutoff p-value and will be removed from the model once p-value larger than the cutoff. This process is done recursively until the model stabilize, with no feature entering and leaving the model. Then the selected features are tuned to maximum RSquare for the validation set. Then Logistic regression is used in building model with the 2 selected peptides.
  • Blast search of the 2 peptides is done using the NCBI blast server.
  • Protein Blast (blastp) suite is used.
  • Database is Reference proteins and organism is limited to Bacteria (taxid:2).
  • Algorithm parameters is set to adjust for short sequences, and max target sequences at 100. Then the matched sequences are processed to contain only linear matched part.
  • the 100 matched sequences are imported into MEME suite to identify epitopes, with configurations of 10 minimum sites per epitope and 3 maximum epitopes.

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Abstract

L'invention concerne des dosages, des réseaux et des procédés permettant de distinguer une infection bactérienne d'une infection virale. La crise antibiotique est en partie entraînée par la prescription excessive d'antibiotiques. Il existe une tendance, en particulier en pédiatrie, à donner un antibiotique même pour des infections virales. Ainsi, des modes de réalisation de la présente invention concernent le problème consistant à distinguer une infection bactérienne d'une infection virale afin de réduire l'utilisation inutile d'antibiotiques.
PCT/US2018/016185 2017-01-31 2018-01-31 Diagnostic pour distinguer des infections bactériennes WO2018144571A2 (fr)

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US16/480,887 US10712342B2 (en) 2017-01-31 2018-01-31 Diagnostic to distinguish bacterial infections
US16/896,117 US11360086B2 (en) 2017-01-31 2020-06-08 Diagnostic to distinguish bacterial infections
US17/806,242 US20220341930A1 (en) 2017-01-31 2022-06-09 Diagnostic to distinguish bacterial infections

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US62/452,825 2017-01-31

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021018810A1 (fr) * 2019-07-26 2021-02-04 Randox Laboratories Ltd Réseau d'agents pathogènes bovins

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018144571A2 (fr) * 2017-01-31 2018-08-09 Arizona Board Of Regents On Behalf Of Arizona State University Diagnostic pour distinguer des infections bactériennes
US12025615B2 (en) 2017-09-15 2024-07-02 Arizona Board Of Regents On Behalf Of Arizona State University Methods of classifying response to immunotherapy for cancer
EP4038222A4 (fr) 2019-10-02 2023-10-18 Arizona Board of Regents on behalf of Arizona State University Procédés et compositions pour identifier des néo-antigènes destinés à être utilisés dans le traitement et la prévention du cancer

Family Cites Families (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040031072A1 (en) * 1999-05-06 2004-02-12 La Rosa Thomas J. Soy nucleic acid molecules and other molecules associated with transcription plants and uses thereof for plant improvement
US20080260763A1 (en) 2004-07-01 2008-10-23 The Regents Of The University Of California High Throughput Proteomics
US8088976B2 (en) * 2005-02-24 2012-01-03 Monsanto Technology Llc Methods for genetic control of plant pest infestation and compositions thereof
WO2007101227A2 (fr) 2006-02-27 2007-09-07 Arizona Board Of Regents For And On Behalf Of Arizona State University Identification et utilisation de novopeptides pour le traitement du cancer
US9732131B2 (en) 2006-02-27 2017-08-15 Calviri, Inc. Identification and use of novopeptides for the treatment of cancer
WO2008048970A2 (fr) 2006-10-16 2008-04-24 The Arizona Board Of Regents, A Body Corporate Of The State Of Arizona Acting For And On Behalf Of Arizona State University Anticorps synthétiques
WO2009126718A2 (fr) 2008-04-08 2009-10-15 Arizona Board Of Regents And On Behalf Of Arizona State University Biomarqueurs associés à une tumeur à partir de la protéome sombre
US20140128280A1 (en) 2008-04-23 2014-05-08 Arizona Board of Regents, a body corporate of the State of Arizona, acting for and on behaif of Ariz Synthetic Antibodies
WO2009140039A2 (fr) 2008-04-23 2009-11-19 The Arizona Board Of Regents Acting For And On Behalf Of Arizona State University Anticorps synthétiques
WO2010028214A2 (fr) 2008-09-04 2010-03-11 Arizona Board Of Regents Acting For And On Behalf Of Arizona State University Procédé de criblage pour identifier des molécules capables de moduler l’activité d’un catalyseur
WO2010027642A2 (fr) 2008-09-04 2010-03-11 Arizona Board Of Regents Sélection de molécules interagissant de façon coopérative
WO2010059958A2 (fr) 2008-11-20 2010-05-27 Arizona Board Of Regents For And On Behalf Of Arizona State University Mimétiques de lectine artificiels et utilisations de ceux-ci
US20120065123A1 (en) 2009-03-24 2012-03-15 Johnston Stephen A Synthetic Antibodies
US20120190566A1 (en) 2009-03-25 2012-07-26 Susan Lindquist Compositions and methods for inhibiting biofilms
WO2010148365A2 (fr) 2009-06-19 2010-12-23 The Arizona Board Of Regents, A Body Corporate Of The State Of Arizona For And On Behalf Of Arizona State University Réseaux de composés pour profilage d'échantillon
WO2011029008A2 (fr) 2009-09-04 2011-03-10 Arizona Board Of Regents Acting For And On Behalf Of Arizona State University Corps de synthèse dirigés contre akt1
US20130224730A1 (en) 2009-11-18 2013-08-29 Arizona Board Of Regents Acting For And On Behalf Of Arizona State University Peptide ligands
US20120316078A1 (en) 2011-06-08 2012-12-13 Sykes Kathryn F Methods and materials for producing polypeptides in vitro
WO2013067160A2 (fr) 2011-11-02 2013-05-10 Arizona Board Of Regents, A Body Corporate Of The State Of Arizona, Acting For And On Behalf Of Arizona State University Polypeptides antibactériens et anticorps synthétiques spécifiques pathogènes
US9482666B2 (en) 2011-11-22 2016-11-01 Arizona Board Of Regents, A Body Corporate Of The State Of Arizona, Acting For And On Behalf Of Arizona State University High throughput selection of specific cell binding and lytic polypeptides
CA2882801C (fr) 2012-08-29 2022-01-18 Arizona Board Of Regents, A Body Corporate Of The State Of Arizona, Acting For And On Behalf Of Arizona State University Immuno-signature : une voie vers le diagnostic precoce et la surveillance de la sante
WO2014039718A1 (fr) 2012-09-05 2014-03-13 Arizona Board of Regents, a body Corporate of the State of Arizona, Acting for and on Behalf of Ariz Procédés de découverte de cibles thérapeutiques
WO2014062981A1 (fr) 2012-10-17 2014-04-24 Arizona Board Of Regents, A Body Corporate Of The State Of Arizona, Acting For And On Behalf Of Arizona State University Formation de motifs in situ par voie chimique
WO2014165240A1 (fr) 2013-03-13 2014-10-09 Arizona Board Of Regents, A Body Corporate Of The State Of Arizona, Acting For & On Behalf Of Corps de synthèse pour la détection des norovirus humains
US10006919B2 (en) 2013-03-15 2018-06-26 Arizona Board Of Regents On Behalf Of Arizona State University Peptide array quality control
WO2014144383A1 (fr) 2013-03-15 2014-09-18 Arizona Board Of Regents, A Body Corporate Of The State Of Arizona, Acting For And On Behalf Of Arizona State University Eléments chimiques mis en forme non covalents et leur utilisation dans le contrôle de qualité à base de maldi
US10011649B2 (en) 2013-08-26 2018-07-03 Arizona Board Of Regents On Behalf Of Arizona State University High affinity synbodies for influenza
WO2015095136A1 (fr) 2013-12-17 2015-06-25 Arizona Board Of Regents On Behalf Of Arizona State University Diagnostic sur la base d'immunosignatures et caractérisation de lymphome canin
US9757472B2 (en) 2014-07-28 2017-09-12 Arizona Board Of Regents On Behalf Of Arizona State University Proteas stabilized antibacterial peptides for S. aureus
US10900975B2 (en) 2015-05-12 2021-01-26 Arizona Board Of Regents On Behalf Of Arizona State University Systems and methods of epitope binning and antibody profiling
US20180284114A1 (en) 2015-10-02 2018-10-04 Arizona Board Of Regents On Behalf Of Arizona State University Methods for processing biopolymeric arrays
US10125167B2 (en) 2015-12-04 2018-11-13 Arizona Board Of Regents On Behalf Of Arizona State University Compositions and methods for treating MRSA infections and for sensitizing MRSA to beta-lactam antibiotics
US10416174B1 (en) 2016-10-26 2019-09-17 Arizona Board Of Regents On Behalf Of Arizona State University Diagnosis and prognosis for chronic fatigue syndrome
WO2018144571A2 (fr) * 2017-01-31 2018-08-09 Arizona Board Of Regents On Behalf Of Arizona State University Diagnostic pour distinguer des infections bactériennes
EP3630130B1 (fr) 2017-06-02 2022-08-31 Arizona Board of Regents on behalf of Arizona State University Procédé de création de vaccins anticancéreux personnalisés
CN110996990A (zh) 2017-06-02 2020-04-10 亚利桑那州立大学董事会 通用癌症疫苗及其制备和使用方法
US20190234962A1 (en) 2017-08-31 2019-08-01 Arizona Board Of Regents On Behalf Of Arizona State University Entropy of immune health
US12025615B2 (en) 2017-09-15 2024-07-02 Arizona Board Of Regents On Behalf Of Arizona State University Methods of classifying response to immunotherapy for cancer
WO2019143712A2 (fr) 2018-01-19 2019-07-25 Arizona Board Of Regents On Behalf Of Arizona State University Réponse d'anticorps commune induite

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021018810A1 (fr) * 2019-07-26 2021-02-04 Randox Laboratories Ltd Réseau d'agents pathogènes bovins
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